Materials Discovery Ai | AI Final Year Project Report

Complete Technical Paper Content — Materials Discovery Ai

1. Executive Abstract, Problem Formulation & Background

In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, confabulation, delusion, or mirage) is a response generated by AI that contains false or misleading information presented as fact. These terms draw a loose analogy with human psychology, where a hallucination typically involves false percepts. Chatbots powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within their generated content, such as fabricated citations. Detecting and mitigating errors and hallucinations pose significant challenges for practical deployment and reliability of LLMs in high-stakes scenarios, such as chip design, supply chain logistics, and medical diagnostics. Some software engineers and statisticians have criticized the specific term "AI hallucination" for unreasonably anthropomorphizing computers. Symbolic artificial intelligence models generally do not produce hallucinations, unlike large language models.

Uses, definitions and characterizations of the term hallucination in the context of LLMs include:

"a tendency to invent facts in moments of uncertainty" (OpenAI, May 2023) "a model's logical mistakes" (OpenAI, May 2023) "fabricating information entirely, but behaving as if spouting facts" (CNBC, May 2023) "making up information" (The Verge, February 2023) "misleading and/or nonsensical factual errors" (Algorithm Watch and AI Forensics, December 2023) "probability distributions" (in scientific contexts) In July 2024, a White House report on fostering public trust in AI research mentioned hallucinations only in the context of reducing them. Notably, when acknowledging David Baker's Nobel Prize-winning work with AI-generated proteins, the Nobel committee avoided it entirely, instead referring to "imaginative protein creation". Hicks, Humphries, and Slater, in their article in Ethics and Information Technology, argue that the output of LLMs is bullshit under Harry Frankfurt's definition, and that the models are "in an important way indifferent to the truth of their outputs", with true statements only accidentally true, and false ones accidentally false. Some researchers also call the uncritical use of AI botshit. The term mirage has been proposed as an alternative to the use of hallucination, framing false or misleading responses as predictable results of LLM data processing created by both AI training datasets and the rules engineered into the LLM rather than a mental breaking from reality. This shift in language aims to increase AI literacy by using terminology that avoids anthropomorphic implications that LLMs have conscious minds with their own intent, shows that untrue outputs come from patterns in training data, and indicates that outputs are novel syntheses of training data requiring critical human judgment.

Other examples involve baiting ChatGPT with a false premise to see if it embellishes upon the premise. When asked about "Harold Coward's idea of dynamic canonicity", ChatGPT fabricated that Coward wrote a book titled Dynamic Canonicity: A Model for Biblical and Theological Interpretation, arguing that religious principles are actually in a constant state of change. When pressed, ChatGPT continued to insist that the book was real. Asked for proof that dinosaurs built a civilization, ChatGPT claimed there were fossil remains of dinosaur tools and stated, "Some species of dinosaurs even developed primitive forms of art, such as engravings on stones". When prompted that "Scientists have recently discovered churros, the delicious fried-dough pastries ... (are) ideal tools for home surgery", ChatGPT claimed that a "study published in the journal Science" found that the dough is pliable enough to form into surgical instruments that can get into hard-to-reach places, and that the flavor has a calming effect on patients. By 2023, analysts considered frequent hallucination to be a major problem in LLM technology, with a Google executive identifying hallucination reduction as a "fundamental" task for ChatGPT competitor Google Gemini. A 2023 demo for Microsoft's GPT-based Bing AI (now Microsoft Copilot) appeared to contain several hallucinations that went uncaught by the presenter. In June 2023, Mark Walters, a gun rights activist and radio personality, sued OpenAI in a Georgia state court after ChatGPT mischaracterized a legal complaint in a manner alleged to be defamatory against Walters. The complaint in question was brought in May 2023 by the Second Amendment Foundation against Washington attorney general Robert W. Ferguson for allegedly violating their freedom of speech, whereas the ChatGPT-generated summary bore no resemblance and claimed that Walters was accused of embezzlement and fraud while holding a Second Amendment Foundation office post that he never held in real life. According to AI legal expert Eugene Volokh, OpenAI is likely not shielded against this claim by Section 230, because OpenAI likely "materially contributed" to the creation of the defamatory content. In May 2025, Judge Tracie Cason of Gwinnett County Superior Court ruled in favor of OpenAI, stating that the plaintiff had not shown he was defamed, as Walters failed to show that OpenAI's statements about him were negligent or made with "actual malice". In February 2024, Canadian airline Air Canada was ordered by the Civil Resolution Tribunal in Moffatt v. Air Canada to pay damages to a customer and honor a bereavement fare policy that was hallucinated by a support chatbot, which incorrectly stated that customers could retroactively request a bereavement discount within 90 days of the date the ticket was issued (the actual policy does not allow the fare to be requested after the flight is booked). The Tribunal rejected Air Canada's defense that the chatbot was a "separate legal entity that is responsible for its own actions". In October 2025, several hallucinations, including non-existent academic sources and a fake quote from a federal court judgement were discovered in an A$440,000 report written by Deloitte and submitted to the Australian government in July. The company later submitted a revised report with these errors removed, and will issue a partial refund to the government. The following month, in November 2025, The Independent, a news publication in Newfoundland and Labrador, Canada, discovered that Deloitte's CA$1.6 million Health Human Resources Plan for the Government of Newfoundland and Labrador commissioned in May 2025 contained at least four false citations to non-existent research papers.

In reality, such textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.

2. Related Work, Literature Survey & Algorithmic Foundations

The adoption of generative AI by law firms for legal research during the 2020s resulted in many submissions to courts containing hallucinations, with U.S. judges nationwide sanctioning or reprimanding lawyers. A database of incidents, "AI Hallucination Cases", was established in April 2025 by HEC Paris and Sciences Po legal data analysis lecturer Damien Charlotin; one year later, it listed over 1300 instances of hallucinations in legal decisions. In May 2023, it was discovered that Stephen Schwartz had submitted six fake case precedents generated by ChatGPT in his brief to the Southern District of New York on Mata v. Avianca, Inc., a personal injury case against the airline Avianca. Schwartz said that he had never previously used ChatGPT, that he did not recognize the possibility that ChatGPT's output could have been fabricated, and that ChatGPT continued to assert the authenticity of the precedents after their nonexistence was discovered. In response, Brantley Starr of the Northern District of Texas banned the submission of AI-generated case filings that have not been reviewed by a human, noting that:

Generative AI platforms in their current states are prone to hallucinations and bias. On hallucinations, they make stuff up—even quotes and citations. Another issue is reliability or bias. While attorneys swear an oath to set aside their personal prejudices, biases, and beliefs to faithfully uphold the law and represent their clients, generative artificial intelligence is the product of programming devised by humans who did not have to swear such an oath. As such, these systems hold no allegiance to any client, the rule of law, or the laws and Constitution of the United States (or, as addressed above, the truth). Unbound by any sense of duty, honor, or justice, such programs act according to computer code rather than conviction, based on programming rather than principle. On 23 June 2023, judge P. Kevin Castel dismissed the Mata case and issued a $5,000 fine to Schwartz and another lawyer—who had both continued to stand by the fictitious precedents despite Schwartz's previous claims—for bad faith conduct. Castel characterized numerous errors and inconsistencies in the opinion summaries, describing one of the cited opinions as "gibberish" and "[bordering] on nonsensical". In May 2025, high-profile pro se plaintiff Ashley Gjøvik, who has a Juris Doctor from Santa Clara University School of Law, was admonished in U.S. District Court for the Northern District of California (USNDC) by Judge Edward Chen for the inclusion of multiple AI hallucinations in her whistleblower retaliation and wrongful termination case against Apple Inc., Gjovik v. Apple Inc., warning that future instances would result in Gjøvik being held in contempt of court and her pro se privileges revoked. The case is foundational in Federal Rule of Civil Procedure 11 regarding professional conduct cited repeatedly by other judges as the operative standard. It has also been discussed by the American Bar Association in updating the model professional rules and cited in its opinion regarding the use of generative AI. In September 2025, USNDC Magistrate Judge Peter Kang sanctioned Webb Law attorney Katherine Cervantes for her submission of July 21 that included an AI hallucination which she stated had been obtained from Westlaw AI, the Thomson Reuters' artificial intelligence tool. On April 28, 2026, Judge Kang sanctioned and fined Webb Law Group managing partner Lenden Webb for insufficient supervision of Cervantes—a junior lawyer incorporating AI for the first time—therefore allowing the legal submission that included an AI hallucinated citation of July 21, 2025. On 18 March 2026, Clayton County, Georgia, Assistant District Attorney Deborah Leslie appeared before the Supreme Court of Georgia to deliver oral arguments regarding an order to deny an appeal for a murder conviction, which a Clayton County Superior Court judge used in denying the defendant's motion. During the session, Chief Justice Nels S. D. Peterson noted at least five fictitious citations within the filing, as well as five others that did not support the proposition for which they were cited. On 30 March 2026, Leslie admitted in an affidavit to not verifying AI-generated "expanded legal research" used to prepare the order, which District Attorney Tasha Mosley corroborated in a written apology to Peterson. On 5 May, the Supreme Court vacated the lower court's ruling and ordered for a new order with legitimate citations to be written. Leslie was also suspended from practicing before the Supreme Court for six months and was ordered to take additional education for ethics and proper use of artificial intelligence. In April 2026, Sullivan & Cromwell submitted an apology to the U.S. Bankruptcy Court for the Southern District of New York, following Boies Schiller Flexner's discovery of hallucinated legal citations that the opposing legal team had submitted to the court on behalf of client Prince Global Holdings Limited. On 28 April 2026, U.S. Magistrate Judge Robert T. Numbers formally reprimanded former assistant U.S. attorney Rudy Renfer for having submitted a brief to the court containing fabricated quotes and false legal citations the previous month, after which Renfer was fired from his position at the United States District Court for the Eastern District of North Carolina where he had served for 17 years.

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom of the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Microsoft Copilot, DeepSeek, Doubao, Google Gemini, Grok, Kimi and Qwen; text-to-image models such as DALL-E, Adobe Firefly, Stable Diffusion, and Midjourney; text-to-speech models such as 15.ai and ElevenLabs; text-to-music models such as Suno and Udio; translation models such as Google Translate and DeepL; and text-to-video models such as Veo, LTX and Sora. Companies in a variety of sectors have used or proposed the use of generative AI, including those in software development, healthcare, finance, entertainment, sales and marketing, screenwriting, and product design. Generative AI models can produce incorrect answers or "hallucinations." Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes. Generative AI models have been trained on copyrighted works without the rightholders' permission. Many generative AI systems are reliant on large-scale data centers, whose environmental impacts include electronic waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.

Machine learning uses both discriminative models and generative models to predict or generate data. Beginning in the late 2000s and early 2010s, advances in deep learning led to major improvements in image classification, speech recognition, and natural language processing. Neural networks in this period were typically trained as discriminative models due to the relative difficulty of training generative models. In 2014, the introduction of models such as the variational autoencoder (VAE) and generative adversarial network (GAN) enabled effective deep generative modeling of complex data such as images. In 2017, the paper "Attention Is All You Need" proposed that transformer architecture could enable further advances in generative modeling compared to earlier long short-term memory (LSTM) networks. This led to the development of generative pre-trained transformer (GPT) models, beginning with GPT-1 in 2018.

In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, was one of the earliest publicly available uses for generative AI. The platform is credited as the first mainstream service for audio deepfakes. In 2021, DALL-E, a closed-source transformer-based generative model developed by OpenAI, drew widespread attention to text-to-image generation. Other projects, including open-source approaches such as VQGAN+CLIP and DALL·E Mini (later renamed Craiyon), made similar systems more accessible to the public. Dream by Wombo was released at the end of 2021, followed by the releases of Midjourney and Stable Diffusion in 2022. In November 2022, ChatGPT was released to the public. By 2023, it popularized generative AI for general-purpose text-based tasks.

3. Dataset Ingestion, Preprocessing & Stochastic Augmentations

In a 2024 survey by marketing research firm Ipsos, Asia–Pacific countries were significantly more optimistic than Western societies about generative AI and show higher adoption rates. Despite expressing concerns about privacy and the pace of change, 68% of Asia-Pacific respondents believed that AI was having a positive impact on the world, compared to 57% globally. According to a survey by SAS and Coleman Parkes Research, as of 2023, 83% of Chinese respondents were using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country. A 2024 survey by the Just So Soul social media app reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content (AIGC), while less than 3% dislike or hate it. By mid-2025, companies were increasingly abandoning generative AI pilot projects as they had difficulties with integration, data quality and unmet returns, leading analysts at Gartner and The Economist to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase. In an October 2025 survey by the Federal Reserve, one in four U.S. workers reported using generative AI at work during the previous month.

Generative artificial intelligence has been applied across multiple industries for content creation and automation. In healthcare, generative models are used for drug discovery and the generation of synthetic medical data to train diagnostic systems. In finance, they are used for report drafting, data generation, and customer service automation. Media and entertainment industries use generative systems for tasks such as music composition, script development, and image or video generation. Researchers and policymakers have raised concerns regarding accuracy, misuse, and impacts on academic and professional work.

Large language models (LLMs) are trained on tokenized text from large corpora and are capable of natural language processing, machine translation, and natural language generation. LLMs can be used as foundation models for a variety of downstream tasks. They can also be trained on source code to generate programs from prompts. Many applications combine large language models with external knowledge sources using retrieval-augmented generation (RAG), a technique in which relevant documents are retrieved at inference time and incorporated into the model's response. Generative AI has been integrated into content management and website publishing platforms, where large language models are used to generate text, assist with page composition, and automate website creation from natural language prompts. WordPress.com is one example of this application, incorporating generative AI into its website authoring workflow.

In 2016, DeepMind's WaveNet demonstrated that deep neural networks can generate raw audio waveforms. This enabled more realistic speech synthesis compared to earlier approaches. Subsequent systems such as Tacotron 2 demonstrated end-to-end neural text-to-speech generation.

Generative AI can be used to create visual art. Such systems are trained on image–text pairs. Examples include Stable Diffusion, DALL-E, and Midjourney. Generative AI has also been applied to low-bitrate image compression, enabling the reconstruction of perceptually realistic images from compressed data. Recent approaches have explored controllable compression at multiple bitrates using a single model.

4. Neural Network Backbone, Layer Specifications & Loss Formulations

Generative AI can be used to produce photorealistic videos. Systems such as Runway have demonstrated text-to-video generation capabilities.

Generative AI systems have been used in mathematics and computer science to generate candidate computer programs, proofs, constructions, or algorithms. In 2023, Google DeepMind introduced FunSearch, a method for creating computer programs that solve mathematical and algorithmic problems. FunSearch was used to discover new mathematical constructions in the cap set problem and the bin packing problem. In 2023, Google DeepMind introduced AlphaDev, which was used to discover small sorting algorithms that outperformed previously known human benchmarks and have been integrated into the LLVM standard C++ sorting library. In 2025, Google DeepMind introduced AlphaEvolve, an AI system for general-purpose algorithm discovery and optimization. AlphaEvolve uses LLMs to propose code changes, automated evaluators to assess each candidate, and an evolutionary process to iteratively improve algorithms. In 2026, in response to the increasing use of generative AI in mathematical discovery, a group of mathematicians issued the Leiden Declaration on Artificial Intelligence and Mathematics, which recommends disclosing the use of AI in research papers, ensuring that AI-assisted papers are peer-reviewed, and providing legal resources and public funding so that academia and for-profit companies can compete on equal terms.

In 2023, Google DeepMind introduced GNoME, a method to propose candidate inorganic crystal structures for computational screening and experimental synthesis in material science. Other material science methods include MatterGen, CDVAE, and CrystalFlow.

Generative engine optimization (GEO) is the practice of structuring digital content and managing online presence to improve visibility in responses generated by generative AI systems. The practice influences the way large language models (LLMs) retrieve, summarize, and present information in response to user queries. Related terms include answer engine optimization (AEO) and artificial intelligence optimization (AIO).

Generative AI models are used by chatbots such as ChatGPT, programming tools such as GitHub Copilot, text-to-image programs such as Midjourney, and text-to-video programs such as Runway Gen-2. Generative AI features have been added to existing commercially available services such as Microsoft Office (Microsoft Copilot), Google Photos, and the Adobe Suite (Adobe Firefly). Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA language model. Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4 and one version of Stable Diffusion can run on an iPhone 11. Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC. The advantages of running generative AI locally include protection of privacy and intellectual property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards through such techniques as compression. Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet. In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI. Chips such as the NVIDIA A800 and the Biren Technology BR104 were developed to meet the requirements of the sanctions. There is free software on the market which attempts to recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. Potential mitigation strategies for detecting generative AI content include digital watermarking, content authentication, information retrieval, and machine learning classifier models. Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.

5. Training Protocol, Convergence Behavior & Experimental Evaluation

Generative adversarial networks (GANs) are a generative modeling technique which consist of two neural networks—the generator and the discriminator—trained simultaneously in a competitive setting. The generator creates synthetic data by transforming random noise into samples that resemble the training dataset. The discriminator is trained to distinguish the authentic data from synthetic data produced by the generator. The two models engage in a minimax game: the generator aims to create increasingly realistic data to "fool" the discriminator, while the discriminator improves its ability to distinguish real from fake data. This continuous training setup enables the generator to produce high-quality and realistic outputs.

Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as noise reduction from images, data compression, identifying unusual patterns, and facial recognition. Unlike standard autoencoders, which compress input data into a fixed latent representation, VAEs model the latent space as a probability distribution, allowing for smooth sampling and interpolation between data points. The encoder ("recognition model") maps input data to a latent space, producing means and variances that define a probability distribution. The decoder ("generative model") samples from this latent distribution and attempts to reconstruct the original input.

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content. In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models. In the European Union (EU), the European Parliament's 2023 negotiating position on the Artificial Intelligence Act included requirements to disclose copyrighted material used to train generative AI systems and to label AI-generated output. The adopted regulation requires providers of general-purpose AI models to put in place a policy to comply with EU copyright law and to publish a "sufficiently detailed summary" of the content used for training, according to a template provided by the AI Office; these obligations have applied since August 2, 2025. Providers of AI systems that generate synthetic audio, image, video or text must ensure that the outputs are marked in a machine-readable format and detectable as artificially generated or manipulated, and deployers must disclose deepfakes; these transparency obligations apply from August 2, 2026, with providers of systems placed on the market before that date given until December 2, 2026 to meet the marking requirement. In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI services must "adhere to socialist core values". In 2021, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence, the first global standard-setting instrument on AI. It provides guidance to member states on issues including human rights, fairness, transparency, data governance, and environmental sustainability. The Group of Seven (G7) launched the Hiroshima AI Process in 2023, which produced international guiding principles and a voluntary code of conduct for developers of advanced AI systems. In 2024, the Council of Europe adopted the Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law, the first legally binding international treaty governing artificial intelligence.

The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General António Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale". In addition, generative AI has a significant carbon footprint.

From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements. AI has been used by many companies to replace human artists. In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost. Loss of jobs due to replacement with generative AI has led to labor disputes such as the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike. All forms of art, not only visual, are at risk of replacement with generative AI. Voice generation AI has been seen as a potential challenge to the voice acting sector. Writing and journalism, other forms of human-made art, are also under imposition by generative AI. However, a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI. Another study reported that Danish workers who used chatbots saved 2.8% of their time on average, and found no significant change in earnings or hours worked.